So far we’ve encountered two ways of writing values: expression statements and
the print() function. (A third way is using the write() method
of file objects; the standard output file can be referenced as sys.stdout.
See the Library Reference for more information on this.)

Often you’ll want more control over the formatting of your output than simply
printing space-separated values. There are two ways to format your output; the
first way is to do all the string handling yourself; using string slicing and
concatenation operations you can create any layout you can imagine. The
string type has some methods that perform useful operations for padding
strings to a given column width; these will be discussed shortly. The second
way is to use formatted string literals, or the
str.format() method.

The string module contains a Template class which offers
yet another way to substitute values into strings.

One question remains, of course: how do you convert values to strings? Luckily,
Python has ways to convert any value to a string: pass it to the repr()
or str() functions.

The str() function is meant to return representations of values which are
fairly human-readable, while repr() is meant to generate representations
which can be read by the interpreter (or will force a SyntaxError if
there is no equivalent syntax). For objects which don’t have a particular
representation for human consumption, str() will return the same value as
repr(). Many values, such as numbers or structures like lists and
dictionaries, have the same representation using either function. Strings, in
particular, have two distinct representations.

(Note that in the first example, one space between each column was added by the
way print() works: by default it adds spaces between its arguments.)

This example demonstrates the str.rjust() method of string
objects, which right-justifies a string in a field of a given width by padding
it with spaces on the left. There are similar methods str.ljust() and
str.center(). These methods do not write anything, they just return a
new string. If the input string is too long, they don’t truncate it, but
return it unchanged; this will mess up your column lay-out but that’s usually
better than the alternative, which would be lying about a value. (If you
really want truncation you can always add a slice operation, as in
x.ljust(n)[:n].)

There is another method, str.zfill(), which pads a numeric string on the
left with zeros. It understands about plus and minus signs:

>>> print('We are the {} who say "{}!"'.format('knights','Ni'))We are the knights who say "Ni!"

The brackets and characters within them (called format fields) are replaced with
the objects passed into the str.format() method. A number in the
brackets can be used to refer to the position of the object passed into the
str.format() method.

>>> print('{0} and {1}'.format('spam','eggs'))spam and eggs>>> print('{1} and {0}'.format('spam','eggs'))eggs and spam

If keyword arguments are used in the str.format() method, their values
are referred to by using the name of the argument.

If you have a really long format string that you don’t want to split up, it
would be nice if you could reference the variables to be formatted by name
instead of by position. This can be done by simply passing the dict and using
square brackets '[]' to access the keys

The % operator can also be used for string formatting. It interprets the
left argument much like a sprintf()-style format string to be applied
to the right argument, and returns the string resulting from this formatting
operation. For example:

>>> importmath>>> print('The value of PI is approximately %5.3f.'%math.pi)The value of PI is approximately 3.142.

open() returns a file object, and is most commonly used with
two arguments: open(filename,mode).

>>> f=open('workfile','w')

The first argument is a string containing the filename. The second argument is
another string containing a few characters describing the way in which the file
will be used. mode can be 'r' when the file will only be read, 'w'
for only writing (an existing file with the same name will be erased), and
'a' opens the file for appending; any data written to the file is
automatically added to the end. 'r+' opens the file for both reading and
writing. The mode argument is optional; 'r' will be assumed if it’s
omitted.

Normally, files are opened in text mode, that means, you read and write
strings from and to the file, which are encoded in a specific encoding. If
encoding is not specified, the default is platform dependent (see
open()). 'b' appended to the mode opens the file in
binary mode: now the data is read and written in the form of bytes
objects. This mode should be used for all files that don’t contain text.

In text mode, the default when reading is to convert platform-specific line
endings (\n on Unix, \r\n on Windows) to just \n. When writing in
text mode, the default is to convert occurrences of \n back to
platform-specific line endings. This behind-the-scenes modification
to file data is fine for text files, but will corrupt binary data like that in
JPEG or EXE files. Be very careful to use binary mode when
reading and writing such files.

It is good practice to use the with keyword when dealing
with file objects. The advantage is that the file is properly closed
after its suite finishes, even if an exception is raised at some
point. Using with is also much shorter than writing
equivalent try-finally blocks:

>>> withopen('workfile')asf:... read_data=f.read()>>> f.closedTrue

If you’re not using the with keyword, then you should call
f.close() to close the file and immediately free up any system
resources used by it. If you don’t explicitly close a file, Python’s
garbage collector will eventually destroy the object and close the
open file for you, but the file may stay open for a while. Another
risk is that different Python implementations will do this clean-up at
different times.

After a file object is closed, either by a with statement
or by calling f.close(), attempts to use the file object will
automatically fail.

The rest of the examples in this section will assume that a file object called
f has already been created.

To read a file’s contents, call f.read(size), which reads some quantity of
data and returns it as a string (in text mode) or bytes object (in binary mode).
size is an optional numeric argument. When size is omitted or negative, the
entire contents of the file will be read and returned; it’s your problem if the
file is twice as large as your machine’s memory. Otherwise, at most size bytes
are read and returned.
If the end of the file has been reached, f.read() will return an empty
string ('').

>>> f.read()'This is the entire file.\n'>>> f.read()''

f.readline() reads a single line from the file; a newline character (\n)
is left at the end of the string, and is only omitted on the last line of the
file if the file doesn’t end in a newline. This makes the return value
unambiguous; if f.readline() returns an empty string, the end of the file
has been reached, while a blank line is represented by '\n', a string
containing only a single newline.

>>> f.readline()'This is the first line of the file.\n'>>> f.readline()'Second line of the file\n'>>> f.readline()''

For reading lines from a file, you can loop over the file object. This is memory
efficient, fast, and leads to simple code:

>>> forlineinf:... print(line,end='')...This is the first line of the file.Second line of the file

If you want to read all the lines of a file in a list you can also use
list(f) or f.readlines().

f.write(string) writes the contents of string to the file, returning
the number of characters written.

>>> f.write('This is a test\n')15

Other types of objects need to be converted – either to a string (in text mode)
or a bytes object (in binary mode) – before writing them:

f.tell() returns an integer giving the file object’s current position in the file
represented as number of bytes from the beginning of the file when in binary mode and
an opaque number when in text mode.

To change the file object’s position, use f.seek(offset,from_what). The position is computed
from adding offset to a reference point; the reference point is selected by
the from_what argument. A from_what value of 0 measures from the beginning
of the file, 1 uses the current file position, and 2 uses the end of the file as
the reference point. from_what can be omitted and defaults to 0, using the
beginning of the file as the reference point.

>>> f=open('workfile','rb+')>>> f.write(b'0123456789abcdef')16>>> f.seek(5)# Go to the 6th byte in the file5>>> f.read(1)b'5'>>> f.seek(-3,2)# Go to the 3rd byte before the end13>>> f.read(1)b'd'

In text files (those opened without a b in the mode string), only seeks
relative to the beginning of the file are allowed (the exception being seeking
to the very file end with seek(0,2)) and the only valid offset values are
those returned from the f.tell(), or zero. Any other offset value produces
undefined behaviour.

File objects have some additional methods, such as isatty() and
truncate() which are less frequently used; consult the Library
Reference for a complete guide to file objects.

Strings can easily be written to and read from a file. Numbers take a bit more
effort, since the read() method only returns strings, which will have to
be passed to a function like int(), which takes a string like '123'
and returns its numeric value 123. When you want to save more complex data
types like nested lists and dictionaries, parsing and serializing by hand
becomes complicated.

Rather than having users constantly writing and debugging code to save
complicated data types to files, Python allows you to use the popular data
interchange format called JSON (JavaScript Object Notation). The standard module called json can take Python
data hierarchies, and convert them to string representations; this process is
called serializing. Reconstructing the data from the string representation
is called deserializing. Between serializing and deserializing, the
string representing the object may have been stored in a file or data, or
sent over a network connection to some distant machine.

Note

The JSON format is commonly used by modern applications to allow for data
exchange. Many programmers are already familiar with it, which makes
it a good choice for interoperability.

If you have an object x, you can view its JSON string representation with a
simple line of code:

Another variant of the dumps() function, called dump(),
simply serializes the object to a text file. So if f is a
text file object opened for writing, we can do this:

json.dump(x,f)

To decode the object again, if f is a text file object which has
been opened for reading:

x=json.load(f)

This simple serialization technique can handle lists and dictionaries, but
serializing arbitrary class instances in JSON requires a bit of extra effort.
The reference for the json module contains an explanation of this.

Contrary to JSON, pickle is a protocol which allows
the serialization of arbitrarily complex Python objects. As such, it is
specific to Python and cannot be used to communicate with applications
written in other languages. It is also insecure by default:
deserializing pickle data coming from an untrusted source can execute
arbitrary code, if the data was crafted by a skilled attacker.